OpenCity: A Scalable Platform to Simulate Urban Activities with Massive LLM Agents
Keywords: LLM Agent, Large Language Model, Urban Study
Abstract: Agent-based models (ABMs) have long been employed to explore how individual behaviors aggregate into complex societal phenomena in urban space. Unlike black-box predictive models, ABMs excel at explaining the micro-macro linkages that drive such emergent behaviors. The recent rise of Large Language Models (LLMs) has led to the development of LLM agents capable of simulating urban activities with unprecedented realism. However, scaling LLM agents to large city simulations presents significant challenges. Existing models are limited by the computational and communication costs of LLMs, compounded by the dynamic nature of urban environments that require continual updates to agent behavior. To address these limitations, we propose OpenCity, a scalable simulation platform optimized for both system and prompt efficiencies. Specifically, we propose a LLM request scheduler to reduce communication overhead by parallelizing requests through IO multiplexing. Besides, we deisgn a ``group-and-distill'' prompt optimization strategy minimizes redundancy by clustering agents with similar static attributes. Through experiments on six global cities, OpenCity achieves a 600-fold acceleration in simulation time per agent, a 70\% reduction in LLM requests, and a 50\% reduction in token usage. These improvements enable the simulation of 10,000 agents’ daily activities in 1 hour on commodity hardware. Additionally, OpenCity establishes a benchmark for LLM agents, comparing simulated mobility behaviors, origin-destination flows, and segregation indices against real-world data. We believe our OpenCity platform provides a critical infrastructure to harness the power of LLMs for interdisciplinary studies in urban space, fostering the collective efforts of broader research communities. Code repo is available at https://anonymous.4open.science/r/Anonymous-OpenCity-42BD.
Primary Area: infrastructure, software libraries, hardware, systems, etc.
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Submission Number: 14253
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